The definition of effective pedagogical strategies for coaching and tutoring students according to their needs is one of the most important issues in Adaptive and Intelligent Educational Systems (AIES). The use of a Reinforcement Learning (RL) model allows the system to learn automatically how to teach to each student individually, only based on the acquired experience with other learners with similar characteristics, like a human tutor does. The application of this artificial intelligence technique, RL, avoids to define the teaching strategies by learning action policies that define what, when and how to teach. In this paper we study the performance of the RL model in a DataBase Design (DBD) AIES, where this performance is measured on number of students required to acquire efficient teaching strategies.
To apply Extended Entity Relationship Model (EER) is a good method for representing requirements on information systems, because of its high level of abstraction. Although it is very close to the user, it is not so trivial when some constructs, such as higher order relationships, are used. This paper describes the characterisation and several important results of an experiment performed at our university in order to show some of the difficulties found when novice students and practitioners use ternary relationships. Some special topics in identifying ternary relationships such as the importance of the domain of text and the intersection data are also investigated. In order to guide and help users in the design task, these results are introduced in PANDORA Case Tool, a research project which tries to serve as a methodological assistance tool.